Our framework is developed upon SeqNet [1]}, one of the state-of-the-art models in person search. We follow SeqNet to feed the output features of the RoI-Align layer to the norm-aware embedding (NAE) [2]} layer. As shown in Fig. REF , the proposed GLCNet network has two individual branches, which are used for extracting global scene context (GSC) and local group context (LGC), respectively.
Based on the assumption that different people have their personal frequently visited places, there may be a probabilistic connection between persons and places/scenes. The more complex and discriminative the scene is, the more unique and robust the connection between it and certain persons is. We find that this phenomenon exists on the existing datasets, especially on the CUHK-SYSU and MovieNet datasets.
With these two branches, the context information could be captured by the global and local features. Specifically, to learn the global features, we design the GSC branch to extract the features from the backbone. Meanwhile, to learn the local features, we employ the LGC branch, which extracts the positive RoI features to learn features of all the persons from a single image. These two features are then fed into the NAE layer to obtain more discriminative features with better diversity.
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